{
 "cells": [
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 5,
=======
   "execution_count": 27,
>>>>>>> 081c522bdcef1cb40c539a5a14ec6d26a3b53059
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import csv\n",
    "import pickle\n",
    "import operator\n",
    "import gc\n",
    "from joblib import Parallel, delayed\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_path = '../../data/ATest_0711.csv'\n",
=======
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_path = '../../data/testData0626.csv'\n",
>>>>>>> 081c522bdcef1cb40c539a5a14ec6d26a3b53059
    "train_data_by_order_path_folder = '../../data/DataForModelB/data_for_train/train_data_by_order'\n",
    "train_data_info_by_test_order_path_folder = '../../data/DataForModelB/data_for_train/train_data_info_by_test_order'"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 7,
=======
   "execution_count": 38,
>>>>>>> 081c522bdcef1cb40c539a5a14ec6d26a3b53059
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_origin = pd.read_csv(test_data_path)\n",
    "test_order_list = test_data_origin['loadingOrder'].unique()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'figure.constrained_layout.use'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-32701b30b79e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasemap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'merc'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mllcrnrlat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m80\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0murcrnrlat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\u001b[0m\u001b[0;34m,\u001b[0m            \u001b[0mllcrnrlon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m180\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0murcrnrlon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m180\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlat_ts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mresolution\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillcontinents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlake_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrawmapboundary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfill_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mfigure\u001b[0;34m(num, figsize, dpi, facecolor, edgecolor, frameon, FigureClass, clear, **kwargs)\u001b[0m\n\u001b[1;32m    531\u001b[0m                                         \u001b[0mframeon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m                                         \u001b[0mFigureClass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFigureClass\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m                                         **kwargs)\n\u001b[0m\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfigLabel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mnew_figure_manager\u001b[0;34m(cls, num, *args, **kwargs)\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFigure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0mfig_cls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FigureClass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_figure_manager_given_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, figsize, dpi, facecolor, edgecolor, linewidth, frameon, subplotpars, tight_layout, constrained_layout)\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_layoutbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[0;31m# set in set_constrained_layout_pads()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_constrained_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconstrained_layout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_tight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mset_constrained_layout\u001b[0;34m(self, constrained)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constrained_layout_pads\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hspace'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconstrained\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m             \u001b[0mconstrained\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'figure.constrained_layout.use'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constrained\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconstrained\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconstrained\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    842\u001b[0m             \u001b[0mst_mode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mst_mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    843\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mS_ISREG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mst_mode\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mS_ISFIFO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mst_mode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 844\u001b[0;31m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    845\u001b[0m     \u001b[0;31m# Return first candidate that is a file, or last candidate if none is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    846\u001b[0m     \u001b[0;31m# valid (in that case, a warning is raised at startup by `rc_params`).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'figure.constrained_layout.use'"
     ]
=======
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program\\Anaconda\\envs\\AI\\lib\\site-packages\\ipykernel_launcher.py:4: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
>>>>>>> 081c522bdcef1cb40c539a5a14ec6d26a3b53059
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,\\\n",
    "            llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')\n",
    "m.fillcontinents(color='gray',lake_color='white')\n",
    "m.drawmapboundary(fill_color='white')\n",
    "\n",
    "for test_order in test_order_list:\n",
    "    path = os.path.join(train_data_info_by_test_order_path_folder, \"{}_related_train_data_info_dump.file\".format(test_order))\n",
    "    with open(path, \"rb\") as f:\n",
    "        train_data_info_by_test = pickle.load(f)\n",
    "    train_data_info_by_test.sort(key=lambda item: item[0])\n",
    "    print(len(train_data_info_by_test))\n",
    "    for train_order_item in train_data_info_by_test:\n",
    "        train_data_gps_path = os.path.join(train_data_by_order_path_folder, \"{}_gps_data.csv\".format(train_order_item[1]))\n",
    "        train_data_gps = pd.read_csv(train_data_gps_path, header=None)\n",
    "        lon = np.array(train_data_gps[1])\n",
    "        lat = np.array(train_data_gps[2])\n",
    "        m.plot(lon,lat,linewidth=2,color='r',latlon='True')\n",
    "        \n",
    "    test_gps_data = test_data_origin[test_data_origin['loadingOrder'] == test_order]\n",
    "    test_lon = test_gps_data['longitude']\n",
    "    test_lat = test_gps_data['latitude']   \n",
    "    m.plot(lon,lat,linewidth=2,color='b',latlon='True')\n",
    "    break\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'figure.constrained_layout.use'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-7e54afd0e985>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"-\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"plot figure\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3350\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0m_autogen_docstring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3351\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3352\u001b[0;31m     \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3353\u001b[0m     \u001b[0;31m# Deprecated: allow callers to override the hold state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3354\u001b[0m     \u001b[0;31m# by passing hold=True|False\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mgca\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    967\u001b[0m     \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0ms\u001b[0m \u001b[0mgca\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m     \"\"\"\n\u001b[0;32m--> 969\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m \u001b[0;31m# More ways of creating axes:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mgcf\u001b[0;34m()\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfigManager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mfigure\u001b[0;34m(num, figsize, dpi, facecolor, edgecolor, frameon, FigureClass, clear, **kwargs)\u001b[0m\n\u001b[1;32m    531\u001b[0m                                         \u001b[0mframeon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m                                         \u001b[0mFigureClass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFigureClass\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m                                         **kwargs)\n\u001b[0m\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfigLabel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mnew_figure_manager\u001b[0;34m(cls, num, *args, **kwargs)\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFigure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0mfig_cls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FigureClass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_figure_manager_given_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, figsize, dpi, facecolor, edgecolor, linewidth, frameon, subplotpars, tight_layout, constrained_layout)\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_layoutbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[0;31m# set in set_constrained_layout_pads()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_constrained_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconstrained_layout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_tight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mset_constrained_layout\u001b[0;34m(self, constrained)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constrained_layout_pads\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hspace'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconstrained\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m             \u001b[0mconstrained\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'figure.constrained_layout.use'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constrained\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconstrained\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconstrained\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    842\u001b[0m             \u001b[0mst_mode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mst_mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    843\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mS_ISREG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mst_mode\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mS_ISFIFO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mst_mode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 844\u001b[0;31m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    845\u001b[0m     \u001b[0;31m# Return first candidate that is a file, or last candidate if none is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    846\u001b[0m     \u001b[0;31m# valid (in that case, a warning is raised at startup by `rc_params`).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'figure.constrained_layout.use'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0.05, 10, 1000)\n",
    "y = np.cos(x)\n",
    "\n",
    "plt.plot(x, y, ls=\"-\", lw=2, label=\"plot figure\")\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
=======
>>>>>>> 081c522bdcef1cb40c539a5a14ec6d26a3b53059
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
<<<<<<< HEAD
   "display_name": "Conda-python3",
   "language": "python",
   "name": "conda-python3"
=======
   "display_name": "Python 3.7.6 64-bit ('AI': conda)",
   "language": "python",
   "name": "python37664bitaiconda6859e03b37c34f0182c9bde8073269f7"
>>>>>>> 081c522bdcef1cb40c539a5a14ec6d26a3b53059
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
<<<<<<< HEAD
   "version": "3.6.4"
=======
   "version": "3.7.6"
>>>>>>> 081c522bdcef1cb40c539a5a14ec6d26a3b53059
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
